A Mouse Model for Eosinophilic Esophagitis (EoE)
Bibliographic record
Abstract
Eosinophilic esophagitis (EoE) is an emerging chronic T helper type 2 (Th2)-associated, allergic, and immune-mediated disease, characterized histologically by eosinophil-predominant mucosal inflammation and clinically by esophageal dysfunction. Over the past years, the prevalence of EoE has dramatically increased globally. Until recently, most studies of EoE focused on using human biopsies, which are also used for diagnostic purposes, or esophageal epithelial cell lines, which led to major advances in the understanding of EoE. Despite this, a robust mouse model that mimics human disease is still crucial for both understanding disease pathogenesis and as a preclinical model for testing future therapeutics. Herein, we describe a highly reproducible and robust model of EoE that can be performed using wild-type mice by ear sensitization with oxazolone (OXA) followed by intraesophageal challenges. Experimental EoE elicited by OXA mimics the main histopathological features of human EoE, including intraepithelial eosinophilia, epithelial and lamina propria thickening, basal cell hyperplasia, and fibrosis. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Induction of EoE in mice using oxazolone Support Protocol 1: Preparing the mouse esophagus for histological analysis Support Protocol 2: Assessment of epithelial and lamina propria thickness using H&E staining Support Protocol 3: Assessment of eosinophilic infiltration using anti-MBP and basal cell proliferation using anti-Ki-67 staining Support Protocol 4: Flow cytometry of mouse esophageal samples Support Protocol 5: ELISA on protein lysates of esophageal samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".